Particle Swarm Optimization for Web Newspaper Layout Problem

J.-H. Lin, C.-K. Wang, and C.-H. Lee (Taiwan)


Particle Swarm Optimization, Chaos, Web Newspaper Lay out


This paper is based on swarm intelligence and chaotic dy namics for learning. We address this issue by considering the problem of web newspaper layout. This problem con sists of optimizing the layout of a set of articles extracted from several web newspapers and sending it to the user as a result of a previous query. This layout should be or ganized in columns as that of real newspapers and should be adapted to the user’s web browser configuration in real time. We propose a new approach to the problem based on an improved particle swarm optimization combined with chaos and chaotic annealing. The particle swarm optimiza tion technique has ever since turned out to be a competitor in the field of numerical optimization. A particle swarm optimization consists of a number of individuals refining their knowledge of the given search space. Particle swarm optimizations are inspired by particles moving around in the search space. By introducing chaotic dynamics to sim ulated annealing, we propose an improved particle swarm optimization with chaotic annealing technique. The key idea of chaotic annealing is to take full advantages of er godic property and stochastic property of chaotic system and replace the Gaussian distribution by chaotic sequences in simulated annealing. This paper proposes some basic concepts inspired by swarm intelligence, chaotic dynamics and simulated annealing for use in computational intelli gence which promises greater efficiency and perhaps solv ability of problems currently not amenable to a optimiza tion approach.

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